Kla Tencor Corporation

Kla Corporation

Tianrong Zhan

Yin Xu

Liequan Lee

Song Qing Wu

Brian C Lin

Jiqiang Li

Andrew Lagodzinski

Methods and systems for selecting measurement locations on a wafer for subsequent detailed measurements employed to characterize the entire wafer are described herein. High throughput measurements are performed at a relatively large number of measurement sites on a wafer. The measurement signals are transformed to a new mathematical basis and reduced to a significantly smaller dimension in the new basis. A set of representative measurement sites is selected based on analyzing variation of the high throughput measurement signals. In some embodiments, the spectra are subdivided into a set of different groups. The spectra are grouped together to minimize variance within each group. Furthermore, a die location is selected that is representative of the variance exhibited by the die in each group. A spectrum of a measurement site and corresponding wafer location is selected to correspond most closely to the center point of each cluster.

Methods and systems for selecting wafer locations to characterize cross-wafer variations based on high-throughput measurement signals

Machine learning techniques are used to predict values of fixed parameters when given reference values of critical parameters. For example, a neural network can be trained based on one or more critical parameters and a low-dimensional real-valued vector associated with a spectrum, such as a spectroscopic ellipsometry spectrum or a specular reflectance spectrum. Another neural network can map the low-dimensional real-valued vector. When using two neural networks, one neural network can be trained to map the spectra to the low-dimensional real-valued vector. Another neural network can be trained to predict the fixed parameter based on the critical parameters and the low-dimensional real-valued vector from the other neural network.

Automatic optimization of measurement accuracy through advanced machine learning techniques

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Shanghai, China

Tianrong Zhan